EXPLAIN PLAN FOR
SELECT sightings.sighting_id, spotters.spotter_name,
sightings.sighting_date
FROM sightings
INNER JOIN spotters
ON sightings.spotter_id = spotters.spotter_id
WHERE sightings.spotter_id = 1255;
SELECT plan_table_output
FROM table(dbms_xplan.display('plan_table',null,'basic'));
id Operation Name
0 select statement
1 nested loops
2 table access by index rowid spotters
3 index unique scan pk_spotter_ID
4 table access full sightings
Im trying to understand whats exactly going on here does this sound right:
First the select statement is evaluated and attributes not in the select list are ignored for the output
Nested loop then computes the inner join on spotters.spotters_id = sightings.spotter_id
Table access by index rowid retrieves the rows with the rowids that were returned by step 3 from the spotters table
Index unique scan, scans spotter_id in PK_SPOTTER_ID index and finds rowids associated rows in the spotters table
Table access full, then scans through sightings completely untill sighting_id = 1255 is found
Steps seem to be basically correct but should be buttom-up.
The projection (choosing the relevant columns) is optimally done as early as possible at the scan phase.
The index operation is SEEK (you are not scanning the whole index)
NOTE: THIS ANSWER REFERS TO THE ORIGINAL VERSION OF THE QUESTION.
Oracle is reading the two tables in their entirety.
It is hashing each of the tables based on the join keys -- "re-ordering" the tables so similar keys appear near each other.
It is doing the join.
It is then doing the calculations for the final select and returning the results to the user.
This is what happens, informally, in the right order:
-- The index pk_spotter_id is scanned for at most one row that satisfies spotter_id = 1255
3 index unique scan pk_spotter_ID
-- The spotter_name column is fetched from the table spotters for the previously found row
2 table access by index rowid spotters
-- A nested loop is run for each (i.e. at most one) of the previously found rows
1 nested loops
-- That nested loop will scan the entire sightings table for rows that match the join
-- predicate sightings.spotter_id = spotters.spotter_id
4 table access full sightings
-- That'll be it for your select statement
0 select statement
In general (there are tons of exceptions), Oracle execution plans can be read
Bottom-up
First sibling first
This means that you go down the tree until you find the first leaf operation (e.g. #3), that'll be executed "first", its results are fed to the parent (e.g. #2), all the siblings are then executed top down, all the siblings' results are also fed to the parent, then the parent result is fed to the grand parent (e.g. #1), until you reach the top operation.
This is a very informal explanation of what happens. Do note there will be many exceptions to these rules once statements become more complex.
Related
This is some pseudo SQL in which the 'problem' is easily replicated:
create table Child (
childId text primary key,
some_int int not null
);
create table Person (
personId text primary key,
childId text,
foreign key (childId) references Child (childId) on delete cascade
);
create index Person_childId on Person (childId);
explain query plan select count(1)
from Person
left outer join Child on Child.childId = Person.childId
where Person.childId is null or Child.some_int = 0;
The result of the query plan is this:
SCAN Person USING COVERING INDEX Person_childId
SEARCH Child USING INDEX sqlite_autoindex_Child_1 (childId=?) LEFT-JOIN
This looks great right? But I am curious if this is the 'full' plan. This is because some_int does not have an index. But the query plan does not uncover this, I don't see the filtering anywhere. The database must filter on this field right?
When I execute the some_int field in a separate query, it shows a SCAN, exactly like I though I would see in the previous query plan because there is no index:
explain query plan select * from Child where some_int = 0;
Gives:
SCAN Child
Now my questions:
Why isn't there SCAN Child shown in the first query plan?
Why is there a SCAN on Person and not a SEARCH?
Is the first query plan 'quick' or do I still need to add an index?
You should take a look at this page. It explains the sqlite query planner in depth and you can find answers to all your questions.
Note that filtering conditions like WHERE some_int=0 are not displayed in the query plan because they don't affect the plan but only the result set.
In brief:
Why isn't there SCAN Child shown in the first query plan?
Because, due to the LEFT JOIN, sqlite needs to SCAN Person and, for every row of Person, use the index on ChildId to find the corresponding records in Child.
Why is there a SCAN on Person and not a SEARCH?
A SCAN means reading of all rows of a table, in the order in which they are stored. A SEARCH is a lookup of a single value in the table, using an index to find out the rowid and the using the rowid to get to that row of the table, without the need to scan all te table to find the row.
Since your query needs to read all Person.childId, it does a full SCAN.
Is the first query plan 'quick' or do I still need to add an index?
Your query is already using all the indexes it could use, so it's already as fast as you could get it.
The problem
Using PostgreSQL 13, I ran into a performance issue selecting the highest id from a view that joins two tables, depending on the select statement I execute.
Here's a sample setup:
CREATE TABLE test1 (
id BIGSERIAL PRIMARY KEY,
joincol VARCHAR
);
CREATE TABLE test2 (
joincol VARCHAR
);
CREATE INDEX ON test1 (id);
CREATE INDEX ON test1 (joincol);
CREATE INDEX ON test2 (joincol);
CREATE VIEW testview AS (
SELECT test1.id,
test1.joincol AS t1charcol,
test2.joincol AS t2charcol
FROM test1, test2
WHERE test1.joincol = test2.joincol
);
What I found out
I'm executing two statements which result in completely different execution plans and runtimes. The following statement executes in less than 100ms. As far as I understand the execution plan, the runtime is independent of the rowcount, since Postgres iterates the rows one by one (starting at the highest id, using the index) until a join on a row is possible and immediately returns.
SELECT id FROM testview ORDER BY ID DESC LIMIT 1;
However, this one takes over 1 second on average (depending on rowcount), since the two tables are "joined completely", before Postgres uses the index to select the highest id.
SELECT MAX(id) FROM testview;
Please refer to this sample on dbfiddle to check the explain plans:
https://www.db-fiddle.com/f/bkMNeY6zXqBAYUsprJ5eWZ/1
My real environment
On my real environment test1 contains only a hand full of rows (< 100), having unique values in joincol. test2 contains up to ~10M rows, where joincol always matches a value of test1's joincol. test2's joincol is not nullable.
The actual question
Why does Postgres not recognize that it could use an Index Scan Backward on row basis for the second select? Is there anything I could improve on the tables/indexes?
Queries not strictly equivalent
why does Postgres not recognize that it could use a Index Scan Backward on row basis for the second select?
To make the context clear:
max(id) excludes NULL values. But ORDER BY ... LIMIT 1 does not.
NULL values sort last in ascending sort order, and first in descending. So an Index Scan Backward might not find the greatest value (according to max()) first, but any number of NULL values.
The formal equivalent of:
SELECT max(id) FROM testview;
is not:
SELECT id FROM testview ORDER BY id DESC LIMIT 1;
but:
SELECT id FROM testview ORDER BY id DESC NULLS LAST LIMIT 1;
The latter query doesn't get the fast query plan. But it would with an index with matching sort order: (id DESC NULLS LAST).
That's different for the aggregate functions min() and max(). Those get a fast plan when targeting table test1 directly using the plain PK index on (id). But not when based on the view (or the underlying join-query directly - the view is not the blocker). An index sorting NULL values in the right place has hardly any effect.
We know that id in this query can never be NULL. The column is defined NOT NULL. And the join in the view is effectively an INNER JOIN which cannot introduce NULL values for id.
We also know that the index on test.id cannot contain NULL values.
But the Postgres query planner is not an AI. (Nor does it try to be, that could get out of hands quickly.) I see two shortcomings:
min() and max() get the fast plan only when targeting the table, regardless of index sort order, an index condition is added: Index Cond: (id IS NOT NULL)
ORDER BY ... LIMIT 1 gets the fast plan only with the exactly matching index sort order.
Not sure, whether that might be improved (easily).
db<>fiddle here - demonstrating all of the above
Indexes
Is there anything I could improve on the tables/indexes?
This index is completely useless:
CREATE INDEX ON "test" ("id");
The PK on test.id is implemented with a unique index on the column, that already covers everything the additional index might do for you.
There may be more, waiting for the question to clear up.
Distorted test case
The test case is too far away from actual use case to be meaningful.
In the test setup, each table has 100k rows, there is no guarantee that every value in joincol has a match on the other side, and both columns can be NULL
Your real case has 10M rows in table1 and < 100 rows in table2, every value in table1.joincol has a match in table2.joincol, both are defined NOT NULL, and table2.joincol is unique. A classical one-to-many relationship. There should be a UNIQUE constraint on table2.joincol and a FK constraint t1.joincol --> t2.joincol.
But that's currently all twisted in the question. Standing by till that's cleaned up.
This is a very good problem, and good testcase.
I tested it in postgres 9.3 perhaps 13 is can it more more fast.
I used Occam's Razor and i excluded some possiblities
View (without view is slow to)
JOIN can filter some rows (unfortunatly in your test not, but more length md5 5-6 yes)
Other basic equivalent select statements not solve yout problem (inner query or exists)
I achieved to use just index, but because the tables isn't bigger than indexes it was not the solution.
I think
CREATE INDEX on "test" ("id");
is useless, because PK!
If you change this
CREATE INDEX on "test" ("joincol");
to this
CREATE INDEX ON TEST (joincol, id);
Than the second query use just indexes.
After you run this
REINDEX table test;
REINDEX table test2;
VACUUM ANALYZE test;
VACUUM ANALYZE test2;
you can achive some performance tuning. Because you created indexes before inserts.
I think the reason is the two aim of DB.
First aim optimalize just some row. So run Nested Loop. You can force it with limit x.
Second aim optimalize whole table. Run this query fast for whole table.
In this situation postgres optimalizer didn't notice that simple MAX can run with NESTED LOOP. Or perhaps postgres cannot use limit in aggregate clause (can run on whole partial select, what is filtered with query).
And this is very expensive. But you have possiblities to write there other aggregates, like SUM, MIN, AVG stb.
Perhaps can help you the Window functions too.
I got the following two queries. One is fast the other is slow.
The table has a clusted index on the Id column.
-- Slow, uses clustered index scan reading 100100 rows
SELECT *
FROM [dbo].[Foo]
ORDER BY Id
OFFSET 100000 ROWS FETCH FIRST 100 ROWS ONLY
-- Fast, uses clustered index seek reading 100 rows
SELECT TOP 100 *
FROM [dbo].[Foo]
WHERE Id > 100000
ORDER BY Id
The plans are identical except for one uses a scan the other a seek.
Can anyone explain why or is this simply how OFFSET works?
The table is very wide with a few NVARCHAR(100-200) and a single NVARCHAR(2500) column.
The two queries are not equivalent. Although you might assume that the ids have no gaps and start at 1, the database engine does not know that.
Indexes are organized to find particular values quickly. They generally do this by traversing a tree structure, and one which is generally balanced. You can read more about this in the documentation.
However, they are not organized to quickly get to the nth row in the table. Hence, the query needs to scan the table to count the number of rows.
That said, the index could do what you want if it kept the number of rows in each child. Do realize that this would complicate modifications to the table, because the entire hierarchy would need to be updated for each update, insert, and delete.
I have this query which takes too much time (since last 1 hour is still running) to execute:
select RL.[LINK_ID] as LINK_ID, RPA.[POSTAL_AREA_ID] as POSTAL_AREA_ID, RRN.[STREET_NAME] as STREET_NAME
from RDF_LINK as RL, RDF_POSTAL_AREA as RPA, RDF_ROAD_LINK as RRL, RDF_ROAD_NAME as RRN
where RRL.[ROAD_NAME_ID] = RRN.[ROAD_NAME_ID]
AND RPA.[POSTAL_AREA_ID] IN (RL.[LEFT_POSTAL_AREA_ID], RL.[RIGHT_POSTAL_AREA_ID])
AND RL.[LINK_ID] = RRL.[LINK_ID]
All the columns which are part of the query are indexed.
The ANALYZE command has already been. performed on database.
The database has approx. 73 millions records in the RDF_ROAD_LINK table and same number of records in other tables.
Is there any other way around to write this query?
EXPLAIN QUERY PLAN
select RL.[LINK_ID] as LINK_ID, RPA.[POSTAL_AREA_ID] as POSTAL_AREA_ID, RRN.[STREET_NAME] as STREET_NAME
from RDF_LINK as RL, RDF_POSTAL_AREA as RPA, RDF_ROAD_LINK as RRL, RDF_ROAD_NAME as RRN
where RRL.[ROAD_NAME_ID] = RRN.[ROAD_NAME_ID]
AND RPA.[POSTAL_AREA_ID] IN (RL.[LEFT_POSTAL_AREA_ID], RL.[RIGHT_POSTAL_AREA_ID])
AND RL.[LINK_ID] = RRL.[LINK_ID]
Output ::
0 0 3 SCAN TABLE RDF_ROAD_NAME AS RRN
0 1 2 SEARCH TABLE RDF_ROAD_LINK AS RRL USING INDEX IND_ROAD_NAME_ID (ROAD_NAME_ID=?)
0 2 0 SEARCH TABLE RDF_LINK AS RL USING INDEX sqlite_autoindex_RDF_LINK_1 (LINK_ID=?)
0 3 1 SEARCH TABLE RDF_POSTAL_AREA AS RPA USING COVERING INDEX sqlite_autoindex_RDF_POSTAL_AREA_1 (POSTAL_AREA_ID=?)
0 0 0 EXECUTE LIST SUBQUERY 1
This query returns all 73 million records, and has to look up the corresponding records from the other tables.
This cannot be fast because there is too much data to be cached (and with this size, it's likely that not even the indexes fit into the cache).
In a join between two tables, the database goes through all rows of the first table, and looks up the corresponding row(s) of the second table.
This means that the first table always ends up with a SCAN, because it would not make sense to use an index (going through an index would not be any faster when you need to load all rows anyway).
In this case, using an index for RDF_ROAD_NAME would be possible only if there were an additional filter on an indexed column (WHERE STREET_NAME = 'My Street'), or if the result must be sorted by an indexed column (ORDER BY ROAD_NAME_ID).
If the tables have many columns that are not used in this query, you might be able to speed it up a little bit by using covering indexes (if all data you need is already in the index, the database does not need to look up the corresponding table row):
CREATE INDEX ... ON RDF_ROAD_LINK(ROAD_NAME_ID, LINK_ID);
CREATE INDEX ... ON RDF_LINK(LINK_ID, LEFT_POSTAL_AREA_ID, RIGHT_POSTAL_AREA_ID);
I've created an Oracle Text index like the following:
create index my_idx on my_table (text) indextype is ctxsys.context;
And I can then do the following:
select * from my_table where contains(text, '%blah%') > 0;
But lets say we have a have another column in this table, say group_id, and I wanted to do the following query instead:
select * from my_table where contains(text, '%blah%') > 0 and group_id = 43;
With the above index, Oracle will have to search for all items that contain 'blah', and then check all of their group_ids.
Ideally, I'd prefer to only search the items with group_id = 43, so I'd want an index like this:
create index my_idx on my_table (group_id, text) indextype is ctxsys.context;
Kind of like a normal index, so a separate text search can be done for each group_id.
Is there a way to do something like this in Oracle (I'm using 10g if that is important)?
Edit (clarification)
Consider a table with one million rows and the following two columns among others, A and B, both numeric. Lets say there are 500 different values of A and 2000 different values of B, and each row is unique.
Now lets consider select ... where A = x and B = y
An index on A and B separately as far as I can tell do an index search on B, which will return 500 different rows, and then do a join/scan on these rows. In any case, at least 500 rows have to be looked at (aside from the database being lucky and finding the required row early.
Whereas an index on (A,B) is much more effective, it finds the one row in one index search.
Putting separate indexes on group_id and the text I feel only leaves the query generator with two options.
(1) Use the group_id index, and scan all the resulting rows for the text.
(2) Use the text index, and scan all the resulting rows for the group_id.
(3) Use both indexes, and do a join.
Whereas I want:
(4) Use the (group_id, "text") index to find the text index under the particular group_id and scan that text index for the particular row/rows I need. No scanning and checking or joining required, much like when using an index on (A,B).
Oracle Text
1 - You can improve performance by creating the CONTEXT index with FILTER BY:
create index my_idx on my_table(text) indextype is ctxsys.context filter by group_id;
In my tests the filter by definitely improved the performance, but it was still slightly faster to just use a btree index on group_id.
2 - CTXCAT indexes use "sub-indexes", and seem to work similar to a multi-column index. This seems to be the option (4) you're looking for:
begin
ctx_ddl.create_index_set('my_table_index_set');
ctx_ddl.add_index('my_table_index_set', 'group_id');
end;
/
create index my_idx2 on my_table(text) indextype is ctxsys.ctxcat
parameters('index set my_table_index_set');
select * from my_table where catsearch(text, 'blah', 'group_id = 43') > 0
This is likely the fastest approach. Using the above query against 120MB of random text similar to your A and B scenario required only 18 consistent gets. But on the downside, creating the CTXCAT index took almost 11 minutes and used 1.8GB of space.
(Note: Oracle Text seems to work correctly here, but I'm not familiar with Text and I can't gaurentee this isn't an inappropriate use of these indexes like #NullUserException said.)
Multi-column indexes vs. index joins
For the situation you describe in your edit, normally there would not be a significant difference between using an index on (A,B) and joining separate indexes on A and B. I built some tests with data similar to what you described and an index join required only 7 consistent gets versus 2 consistent gets for the multi-column index.
The reason for this is because Oracle retrieves data in blocks. A block is usually 8K, and an index block is already sorted, so you can probably fit the 500 to 2000 values in a few blocks. If you're worried about performance, usually the IO to read and write blocks is the only thing that matters. Whether or not Oracle has to join together a few thousand rows is an inconsequential amount of CPU time.
However, this doesn't apply to Oracle Text indexes. You can join a CONTEXT index with a btree index (a "bitmap and"?), but the performance is poor.
I'd put an index on group_id and see if that's good enough. You don't say how many rows we're talking about or what performance you need.
Remember, the order in which the predicates are handled is not necessarily the order in which you wrote them in the query. Don't try to outsmart the optimizer unless you have a real reason to.
Short version: There's no need to do that. The query optimizer is smart enough to decide what's the best way to select your data. Just create a btree index on group_id, ie:
CREATE INDEX my_group_idx ON my_table (group_id);
Long version: I created a script (testperf.sql) that inserts 136 rows of dummy data.
DESC my_table;
Name Null Type
-------- -------- ---------
ID NOT NULL NUMBER(4)
GROUP_ID NUMBER(4)
TEXT CLOB
There is a btree index on group_id. To ensure the index will actually be used, run this as a dba user:
EXEC DBMS_STATS.GATHER_TABLE_STATS('<YOUR USER HERE>', 'MY_TABLE', cascade=>TRUE);
Here's how many rows each group_id has and the corresponding percentage:
GROUP_ID COUNT PCT
---------------------- ---------------------- ----------------------
1 1 1
2 2 1
3 4 3
4 8 6
5 16 12
6 32 24
7 64 47
8 9 7
Note that the query optimizer will use an index only if it thinks it's a good idea - that is, you are retrieving up to a certain percentage of rows. So, if you ask it for a query plan on:
SELECT * FROM my_table WHERE group_id = 1;
SELECT * FROM my_table WHERE group_id = 7;
You will see that for the first query, it will use the index, whereas for the second query, it will perform a full table scan, since there are too many rows for the index to be effective when group_id = 7.
Now, consider a different condition - WHERE group_id = Y AND text LIKE '%blah%' (since I am not very familiar with ctxsys.context).
SELECT * FROM my_table WHERE group_id = 1 AND text LIKE '%ipsum%';
Looking at the query plan, you will see that it will use the index on group_id. Note that the order of your conditions is not important:
SELECT * FROM my_table WHERE text LIKE '%ipsum%' AND group_id = 1;
Generates the same query plan. And if you try to run the same query on group_id = 7, you will see that it goes back to the full table scan:
SELECT * FROM my_table WHERE group_id = 7 AND text LIKE '%ipsum%';
Note that stats are gathered automatically by Oracle every day (it's scheduled to run every night and on weekends), to continually improve the effectiveness of the query optimizer. In short, Oracle does its best to optimize the optimizer, so you don't have to.
I do not have an Oracle instance at hand to test, and have not used the full-text indexing in Oracle, but I have generally had good performance with inline views, which might be an alternative to the sort of index you had in mind. Is the following syntax legit when contains() is involved?
This inline view gets you the PK values of the rows in group 43:
(
select T.pkcol
from T
where group = 43
)
If group has a normal index, and doesn't have low cardinality, fetching this set should be quick. Then you would inner join that set with T again:
select * from T
inner join
(
select T.pkcol
from T
where group = 43
) as MyGroup
on T.pkcol = MyGroup.pkcol
where contains(text, '%blah%') > 0
Hopefully the optimizer would be able to use the PK index to optimize the join and then appy the contains predicate only to the group 43 rows.